While for many years two alternative approaches to building intelligent systems, symbolic AI and neural networks, have each demonstrated specific advantages and also revealed specific weaknesses, in recent years a number of researchers have sought methods of combining the two into a unified methodology which embodies the benefits of each while attenuating the disadvantages. This work sets out to identify the key ideas from each discipline and combine them into an architecture which would be practically scalable for very large network applications. The architecture is based on a relational database structure and forms the environment for an investigation into the necessary properties of a symbol encoding which will permit the singlepresentation learning of patterns and associations, the development of categories and features leading to robust generalisation and the seamless integration of a range of memory persistencies from short to long term. It is argued that if, as proposed by many proponents of symbolic AI, the symbol encoding must be causally related to its syntactic meaning, then it must also be mutable as the network learns and grows, adapting to the growing complexity of the relationships in which it is instantiated. Furthermore, it is argued that in order to create an efficient and coherent memory structure, the symbolic encoding itself must have an underlying structure which is not accessible symbolically; this structure would provide the framework permitting structurally sensitive processes to act upon symbols without explicit reference to their content. Such a structure must dictate how new symbols are created during normal operation. The network implementation proposed is based on K-from-N codes, which are shown to possess a number of desirable qualities and are well matched to the requirements of the symbol encoding. Several networks are developed and analysed to exploit these codes, based around a recurrent version of the non-holographic associati ve memory of Willshaw, et al. The simplest network is shown to have properties similar to those of a Hopfield network, but the storage capacity is shown to be greater, though at a cost of lower signal to noise ratio. Subsequent network additions break each K-from-N pattern into L subsets, each using D-from-N coding, creating cyclic patterns of period L. This step increases the capacity still further but at a cost of lower signal to noise ratio. The use of the network in associating pairs of input patterns with any given output pattern, an architectural requirement, is verified. The use of complex synaptic junctions is investigated as a means to increase storage capacity, to address the stability-plasticity dilemma and to implement the hierarchical aspects of the symbol encoding defined in the architecture. A wide range of options is developed which allow a number of key global parameters to be traded-off. One scheme is analysed and simulated. A final section examines some of the elements that need to be added to our current understanding of neural network-based reasoning systems to make general purpose intelligent systems possible. It is argued that the sections of this work represent pieces of the whole in this regard and that their integration will provide a sound basis for making such systems a reality.